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<body>
<pre><code class="r">##############################################################
##############################################################
### Replication Materials for
### Stefan Müller and Liam Kneafsey:
### Evidence for the Irrelevance of Irrelevant Events
### Political Science Research and Methods
###
### Please get in touch with the authors if you have any questions: 
### stefan.mueller@ucd.ie
### Note: 0000_readme.pdf contains 
### instructions and details about each script and dataset
##############################################################
##############################################################

## load packages
library(dplyr)      # CRAN v1.0.5
library(ggplot2)    # CRAN v3.3.3
library(estimatr)   # CRAN v0.30.2
library(broom)      # CRAN v0.7.4
library(here)       # CRAN v1.0.1
library(parameters) # CRAN v0.13.0
library(stringr)    # CRAN v1.4.0

here::here()
</code></pre>

<pre><code>## [1] &quot;/Users/smueller/Dropbox/papers/ireland_incumbency_gaa&quot;
</code></pre>

<pre><code class="r">## alternatively, set working directory manually
## setwd(&quot;...&quot;)

## load custom ggplot2 theme
source(&quot;function_theme_base.R&quot;)


## load local election results
dat_local_raw &lt;- readRDS(&quot;data_elections_local_full.rds&quot;) 

## remove candidates who did not compete in two subsequent elections
dat_local &lt;- filter(dat_local_raw, !is.na(diff_votes))

dat_general_raw &lt;- readRDS(&quot;data_elections_general_full.rds&quot;)

## remove candidates who did not compete in two subsequent elections
dat_general &lt;- filter(dat_general_raw, !is.na(diff_votes))


## keep only treated incumbents in general elections
dat_general_incumbents_treated &lt;- dat_general %&gt;% 
    filter(winner_loser_before != &quot;Untreated&quot;) 

## keep only treated candidates in local elections
dat_local_incumbents_treated &lt;- dat_local %&gt;% 
    filter(winner_loser_before != &quot;Untreated&quot;) 


## keep only treated candidates 
dat_general_partyincumbent_treated &lt;- dat_general %&gt;% 
    filter(winner_loser_before != &quot;Untreated&quot;) 



## run models for equivalence test and retrieve the coefficient of winner_loser_before

## general elections: effect of win on support for incumbents
lm_cand_general_share &lt;- estimatr::lm_robust(diff_share ~ 
                                                 winner_loser_before * incumbent +
                                                 party_recoded + election_id + 
                                                 county_gaa,
                                             clusters = county_gaa,
                                             se_type = &quot;stata&quot;,
                                             data = dat_general_incumbents_treated)

lm_cand_general_share_tidy &lt;- broom::tidy(lm_cand_general_share) %&gt;% 
    filter(term == &quot;winner_loser_beforeWin:incumbentIncumbent (Candidate elected in t-1)&quot;)




## general elections: effect of win on support for incumbents
lm_eq_general &lt;- lm(diff_share ~ 
                        winner_loser_before * incumbent +
                        party_recoded + election_id + 
                        county_gaa,
                    data = dat_general_incumbents_treated)


lm_eq_local &lt;- lm(diff_share ~ 
                      winner_loser_before * incumbent + 
                      party_recoded + election_id + 
                      county_gaa,
                  data = dat_local_incumbents_treated)

## relevel variable to ensure that the interaction coefficient reports candidate from government party * winner
dat_general_partyincumbent_treated$incumbency_status_party &lt;- relevel(factor(dat_general_partyincumbent_treated$incumbency_status_party),
                                                                      ref = &quot;Party: Opposition&quot;)

lm_eq_partyincumbent &lt;- lm(diff_share ~ 
                               winner_loser_before * incumbency_status_party +
                               party_recoded + election_id + 
                               county_gaa,
                           data = dat_general_partyincumbent_treated)


sd_general &lt;- 0.3 * sd(dat_general_incumbents_treated$diff_share,
                       na.rm = TRUE) 
sd_general
</code></pre>

<pre><code>## [1] 1.644685
</code></pre>

<pre><code class="r">eq_general &lt;- equivalence_test(lm_eq_general,
                               rule = &quot;classic&quot;,
                               ci = 0.95,
                               range = c(-sd_general, sd_general)
) %&gt;%
    as.data.frame() %&gt;%
    filter(str_detect(Parameter, &quot;winner_loser_beforeWin:incumbent&quot;)) %&gt;% 
    mutate(model = &quot;General Elections: Incumbents x Win&quot;)


sd_local &lt;- 0.3 * sd(dat_local_incumbents_treated$diff_share,
                     na.rm = TRUE) 
sd_local
</code></pre>

<pre><code>## [1] 1.456027
</code></pre>

<pre><code class="r">eq_local &lt;- equivalence_test(lm_eq_local,
                             rule = &quot;classic&quot;,
                             ci = 0.95,
                             range = c(-sd_local, sd_local),
) %&gt;%
    as.data.frame() %&gt;%
    filter(str_detect(Parameter, &quot;winner_loser_beforeWin:incumbent&quot;)) %&gt;% 
    mutate(model = &quot;Local Elections: Incumbents x Win&quot;)


sd_partyinc &lt;- 0.3 * sd(dat_general_partyincumbent_treated$diff_share,
                        na.rm = TRUE)
sd_partyinc
</code></pre>

<pre><code>## [1] 1.644685
</code></pre>

<pre><code class="r">eq_partyinc &lt;- equivalence_test(lm_eq_partyincumbent,
                                rule = &quot;classic&quot;,
                                ci = 0.95,
                                range = c(-sd_partyinc, sd_partyinc)
) %&gt;%
    as.data.frame() %&gt;%
    filter(str_detect(Parameter, &quot;winner_loser_beforeWin:incumbency_status_partyParty: Government&quot;)) %&gt;% 
    mutate(model = &quot;General Elections: Candidates from Government Parties x Win&quot;)


eq_combined &lt;- bind_rows(eq_general, 
                         eq_local,
                         eq_partyinc) %&gt;% 
    mutate(estimate = (CI_high + CI_low) / 2)


## Figure A18 ----
ggplot(filter(eq_combined, str_detect(model, &quot;General Elections: Incumbents x Win&quot;)),
       aes(x = &quot;&quot;, y = estimate, 
           ymin = CI_low, ymax = CI_high)) +
    geom_hline(aes(yintercept = ROPE_low),
               linetype = &quot;dashed&quot;, colour = &quot;grey50&quot;) +
    geom_hline(aes(yintercept = ROPE_high),
               linetype = &quot;dashed&quot;, colour = &quot;grey50&quot;) +
    geom_hline(yintercept = 0, colour = &quot;red&quot;) +
    geom_point(size = 4) +
    geom_linerange(size = 1.05) +
    facet_wrap(~model) +
    annotate(&quot;segment&quot;, x = 0.5, xend = 0.5, y = -0.9, yend = -1.55,
             colour = &quot;grey60&quot;, size = 0.8, 
             arrow = arrow(angle = 30,
                           length = unit(0.15, &quot;inches&quot;))) +
    annotate(&quot;segment&quot;, x = 0.5, xend = 0.5, y = 0.9, yend = 1.55,
             colour = &quot;grey60&quot;, size = 0.8, 
             arrow = arrow(angle = 30,
                           length = unit(0.15, &quot;inches&quot;))) +
    annotate(&quot;text&quot;, x = 0.7, y = sd_local - 0.05, hjust = 1,
             colour = &quot;grey50&quot;,
             label = &quot;Upper equivalence bound\n(+0.3 SD of DV)&quot;) +
    annotate(&quot;text&quot;, x = 0.7, y = -sd_local  + 0.05, hjust = 0,
             colour = &quot;grey50&quot;,
             label = &quot;Lower equivalence bound\n(-0.3 SD of DV)&quot;) +
    coord_flip() +
    scale_y_continuous(limits = c(-3, 3), breaks = c(seq(-3, 3, 0.5))) +
    labs(x = NULL, y = &quot;Effect size&quot;) +
    theme(axis.ticks.y = element_blank())
</code></pre>

<p><img 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" alt="plot of chunk unnamed-chunk-1"/></p>

<pre><code class="r">ggsave(&quot;fig_a18.pdf&quot;,
       width = 9, height = 3)


## Figure A19 ----
ggplot(filter(eq_combined, str_detect(model, &quot;Local&quot;)),
       aes(x = &quot;&quot;, y = estimate, 
           ymin = CI_low, ymax = CI_high)) +
    geom_hline(aes(yintercept = ROPE_low),
               linetype = &quot;dashed&quot;, colour = &quot;grey50&quot;) +
    geom_hline(aes(yintercept = ROPE_high),
               linetype = &quot;dashed&quot;, colour = &quot;grey50&quot;) +
    geom_hline(yintercept = 0, colour = &quot;red&quot;) +
    geom_point(size = 4) +
    geom_linerange(size = 1.05) +
    facet_wrap(~model) +
    annotate(&quot;segment&quot;, x = 0.5, xend = 0.5, y = -0.9, yend = -1.4,
             colour = &quot;grey60&quot;, size = 0.8, 
             arrow = arrow(angle = 30,
                           length = unit(0.15, &quot;inches&quot;))) +
    annotate(&quot;segment&quot;, x = 0.5, xend = 0.5, y = 0.9, yend = 1.4,
             colour = &quot;grey60&quot;, size = 0.8, 
             arrow = arrow(angle = 30,
                           length = unit(0.15, &quot;inches&quot;))) +
    annotate(&quot;text&quot;, x = 0.7, y = sd_local - 0.05, hjust = 1,
             colour = &quot;grey50&quot;,
             label = &quot;Upper equivalence bound\n(+0.3 SD of DV)&quot;) +
    annotate(&quot;text&quot;, x = 0.7, y = -sd_local  + 0.05, hjust = 0,
             colour = &quot;grey50&quot;,
             label = &quot;Lower equivalence bound\n(-0.3 SD of DV)&quot;) +
    coord_flip() +
    scale_y_continuous(limits = c(-3, 3), breaks = c(seq(-3, 3, 0.5))) +
    labs(x = NULL, y = &quot;Effect size&quot;) +
    theme(axis.ticks.y = element_blank())
</code></pre>

<p><img 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" alt="plot of chunk unnamed-chunk-1"/></p>

<pre><code class="r">ggsave(&quot;fig_a19.pdf&quot;,
       width = 9, height = 3)


## Figure A20 ----
ggplot(filter(eq_combined, str_detect(model, &quot;Government&quot;)),
       aes(x = &quot;&quot;, y = estimate, 
           ymin = CI_low, ymax = CI_high)) +
    geom_hline(aes(yintercept = ROPE_low),
               linetype = &quot;dashed&quot;, colour = &quot;grey50&quot;) +
    geom_hline(aes(yintercept = ROPE_high),
               linetype = &quot;dashed&quot;, colour = &quot;grey50&quot;) +
    geom_hline(yintercept = 0, colour = &quot;red&quot;) +
    geom_point(size = 4) +
    geom_linerange(size = 1.05) +
    facet_wrap(~model) +
    annotate(&quot;segment&quot;, x = 0.5, xend = 0.5, y = -0.9, yend = -1.55,
             colour = &quot;grey60&quot;, size = 0.8, 
             arrow = arrow(angle = 30,
                           length = unit(0.15, &quot;inches&quot;))) +
    annotate(&quot;segment&quot;, x = 0.5, xend = 0.5, y = 0.9, yend = 1.55,
             colour = &quot;grey60&quot;, size = 0.8, 
             arrow = arrow(angle = 30,
                           length = unit(0.15, &quot;inches&quot;))) +
    annotate(&quot;text&quot;, x = 0.7, y = sd_partyinc - 0.1, hjust = 1,
             colour = &quot;grey50&quot;,
             label = &quot;Upper equivalence bound\n(+0.3 SD of DV)&quot;) +
    annotate(&quot;text&quot;, x = 0.7, y = -sd_partyinc + 0.1, hjust = 0,
             colour = &quot;grey50&quot;,
             label = &quot;Lower equivalence bound\n(-0.3 SD of DV)&quot;) +
    coord_flip() +
    scale_y_continuous(limits = c(-3, 3), breaks = c(seq(-3, 3, 0.5))) +
    labs(x = NULL, y = &quot;Effect size&quot;) +
    theme(axis.ticks.y = element_blank())
</code></pre>

<p><img 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" alt="plot of chunk unnamed-chunk-1"/></p>

<pre><code class="r">ggsave(&quot;fig_a20.pdf&quot;,
       width = 9, height = 3)


## Permutation tests ----


## general elections

#                   # create data frame with one observation per constituency and election 
## and the &quot;treatment&quot;

dat_general_treated &lt;- dat_general %&gt;% 
    mutate(const_election_id = paste(election_id, constituency_name, sep = &quot;_&quot;)) %&gt;% 
    filter(winner_loser_before != &quot;Untreated&quot;) # remove untreated constituencies

## get a data frame on the level of constituencies that indicates 
## the treatment for this constituency
dat_general_constituencies_treated &lt;- dat_general_treated %&gt;% 
    ungroup() %&gt;% 
    select(election_id, constituency_name, winner_loser_before) %&gt;% 
    unique() %&gt;% 
    arrange(election_id, constituency_name) %&gt;% 
    mutate(const_election_id = paste(election_id, constituency_name, sep = &quot;_&quot;))

nrow(dat_general_constituencies_treated)
</code></pre>

<pre><code>## [1] 169
</code></pre>

<pre><code class="r">## draw 1000 random seeds
set.seed(2356)
seeds &lt;- sample(1:1000, replace = FALSE)

dat_coefficients_permutation_general &lt;- data.frame()

for (i in seeds) {

    ## cat(&quot;Permutating treatments using seed #&quot;, i, &quot;\n&quot;)
    set.seed(i)

    ## for each election (group_by(election_id)) reshuffle the treatments on the level of
    ## constituencies 
    dat_const_resampled_outcome_general &lt;- dat_general_constituencies_treated %&gt;% 
        group_by(election_id) %&gt;% 
        mutate(winner_loser_before_simulated = winner_loser_before[sample(row_number())], 
               replace = FALSE) %&gt;% 
        ungroup() %&gt;% 
        select(const_election_id, winner_loser_before_simulated) ## select constituency identified and simulated treatment

    dat_general_joined &lt;- left_join(dat_general_treated, dat_const_resampled_outcome_general,
                                    by = &quot;const_election_id&quot;)

    ## run regression model with simulated treatment
    lm_diff_share_sampled_general &lt;- estimatr::lm_robust(
        diff_share ~ 
            winner_loser_before_simulated * incumbent +
            party_recoded + election_id + 
            county_gaa,
        clusters = county_gaa,
        se_type = &quot;stata&quot;,
        data = dat_general_joined)

    ## tidy regression output
    lm_result_tidy_general &lt;- broom::tidy(lm_diff_share_sampled_general)

    ## indicator for seed
    lm_result_tidy_general$seed &lt;- i

    ## bind simulations
    dat_coefficients_permutation_general &lt;- bind_rows(lm_result_tidy_general,
                                                      dat_coefficients_permutation_general)
}



## run regression model with real treatment
lm_cand_share_true_general_all &lt;- estimatr::lm_robust(
    diff_share ~ 
        winner_loser_before * incumbent +
        party_recoded + election_id + 
        county_gaa,
    clusters = county_gaa,
    se_type = &quot;stata&quot;,
    data = dat_general_treated)

lm_result_tidy_true_general &lt;- broom::tidy(lm_cand_share_true_general_all)


## get coefficient of interaction between simulated treatment and incumbent
dat_coefficients_permutation_general_int &lt;- filter(dat_coefficients_permutation_general,
                                                   term == &quot;winner_loser_before_simulatedWin:incumbentIncumbent (Candidate elected in t-1)&quot;) 


## number of simulations (nrow should be 1,000)
nrow(dat_coefficients_permutation_general_int) 
</code></pre>

<pre><code>## [1] 1000
</code></pre>

<pre><code class="r">## get coefficient of &quot;true&quot; interaction 
lm_result_tidy_true_general &lt;- filter(lm_result_tidy_true_general, 
                                      term == &quot;winner_loser_beforeWin:incumbentIncumbent (Candidate elected in t-1)&quot;)
nrow(lm_result_tidy_true_general)
</code></pre>

<pre><code>## [1] 1
</code></pre>

<pre><code class="r">## Figure A21 ----

#                   # create a plot with the distribution of simulated coefficients
## and the true interaction coefficient
ggplot(dat_coefficients_permutation_general_int, aes(x = estimate)) +
    geom_histogram(colour = &quot;grey50&quot;, fill = &quot;grey50&quot;) + 
    geom_vline(data = lm_result_tidy_true_general, 
               aes(xintercept = estimate), 
               size = 1.5, 
               colour = &quot;red&quot;) +
    geom_vline(xintercept = 0, linetype = &quot;dashed&quot;,
               colour = &quot;black&quot;) +
    scale_x_continuous(limits = c(-2.3, 2.3)) +
    annotate(&quot;text&quot;, x = -0.7,
             colour = &quot;white&quot;,
             y = 10, label = &quot;Coefficients using\nrandomly permutated treatments&quot;) +
    annotate(&quot;text&quot;, x = lm_result_tidy_true_general$estimate + 0.04,
             hjust = 0,
             colour = &quot;red&quot;,
             y = 100, label = &quot;Coefficient using real treatment&quot;) +
    labs(x = &quot;Coefficient of interaction term (Win x Candidate elected in t-1)&quot;, y = &quot;Count&quot;) 
</code></pre>

<pre><code>## `stat_bin()` using `bins =
## 30`. Pick better value with
## `binwidth`.
</code></pre>

<p><img 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" alt="plot of chunk unnamed-chunk-1"/></p>

<pre><code class="r">ggsave(&quot;fig_a21.pdf&quot;,
       width = 9, height = 4)
</code></pre>

<pre><code>## `stat_bin()` using `bins =
## 30`. Pick better value with
## `binwidth`.
</code></pre>

<pre><code class="r">## repeat the same analysis for local elections

dat_local_treated &lt;- dat_local %&gt;% 
    mutate(const_election_id = paste(election_id, const_link, sep = &quot;_&quot;)) %&gt;% 
    filter(winner_loser_before != &quot;Untreated&quot;)

dat_local_constituencies_treated &lt;- dat_local_treated %&gt;% 
    ungroup() %&gt;% 
    select(election_id, const_link, winner_loser_before) %&gt;% 
    unique() %&gt;% 
    arrange(election_id, const_link) %&gt;% 
    mutate(const_election_id = paste(election_id, const_link, sep = &quot;_&quot;))

set.seed(2356)
seeds &lt;- sample(1:1000, replace = FALSE)

dat_coefficients_permutation_local &lt;- data.frame()
for (i in seeds) {

    ## cat(&quot;Permutating treatments using seed #&quot;, i, &quot;\n&quot;)
    set.seed(i)

    ## for each election (group_by(election_id)) reshuffle the treatments on the level of
    ## constituencies 
    dat_const_resampled_outcome_local &lt;- dat_local_constituencies_treated %&gt;% 
        group_by(election_id) %&gt;% 
        mutate(winner_loser_before_simulated = winner_loser_before[sample(row_number())], 
               replace = FALSE) %&gt;% 
        ungroup() %&gt;% 
        select(const_election_id, winner_loser_before_simulated) ## select constituency identified and simulated treatment

    dat_local_joined &lt;- left_join(dat_local_treated, dat_const_resampled_outcome_local,
                                  by = &quot;const_election_id&quot;)

    ## run regression model with simulated treatment
    lm_diff_share_sampled_local &lt;- estimatr::lm_robust(
        diff_share ~ 
            winner_loser_before_simulated * incumbent +
            party_recoded + election_id + 
            county_gaa,
        clusters = county_gaa,
        se_type = &quot;stata&quot;, 
        data = dat_local_joined)

    ## tidy regression output
    lm_result_tidy_local &lt;- broom::tidy(lm_diff_share_sampled_local)

    ## indicator for seed
    lm_result_tidy_local$seed &lt;- i

    ## bind rows
    dat_coefficients_permutation_local &lt;- bind_rows(lm_result_tidy_local,
                                                    dat_coefficients_permutation_local)
}


## run regression model with real treatment
lm_cand_share_true_local_all &lt;- estimatr::lm_robust(
    diff_share ~ 
        winner_loser_before * incumbent +
        party_recoded + election_id + 
        county_gaa,
    clusters = county_gaa,
    se_type = &quot;stata&quot;,
    data = dat_local_treated)


lm_result_tidy_true_local &lt;- broom::tidy(lm_cand_share_true_local_all)

## get coefficient of simulated interaction term
dat_coefficients_permutation_local_int &lt;- filter(dat_coefficients_permutation_local,
                                                 term == &quot;winner_loser_before_simulatedWin:incumbentIncumbent (Candidate elected in t-1)&quot;) 


## number of simulations (nrow should be 1,000)
nrow(dat_coefficients_permutation_local_int)
</code></pre>

<pre><code>## [1] 1000
</code></pre>

<pre><code class="r">## get coefficient of &quot;true&quot; interaction 
lm_result_tidy_true_local &lt;- filter(lm_result_tidy_true_local, 
                                    term == &quot;winner_loser_beforeWin:incumbentIncumbent (Candidate elected in t-1)&quot;)
nrow(lm_result_tidy_true_local)
</code></pre>

<pre><code>## [1] 1
</code></pre>

<pre><code class="r">## Figure A22 ----

#                   # create a plot with the distribution of simulated coefficients
## and the true interaction coefficient
ggplot(dat_coefficients_permutation_local_int, aes(x = estimate)) +
    geom_histogram(colour = &quot;grey50&quot;, fill = &quot;grey50&quot;) + 
    geom_vline(data = lm_result_tidy_true_local, 
               aes(xintercept = estimate), 
               size = 1.5, 
               colour = &quot;red&quot;) +
    geom_vline(xintercept = 0, colour = &quot;black&quot;,
               linetype = &quot;dashed&quot;) +
    annotate(&quot;text&quot;, x = 0.5,
             size = 3,
             colour = &quot;white&quot;,
             y = 30, label = &quot;Coefficients\nusing\nrandomly\npermutated\ntreatments&quot;) +
    annotate(&quot;text&quot;, x = lm_result_tidy_true_local$estimate + 0.04,
             hjust = 0,
             colour = &quot;red&quot;,
             y = 200, label = &quot;Coefficient using real treatment&quot;) +
    scale_x_continuous(limits = c(-2.3, 2.3)) +
    labs(x = &quot;Coefficient of interaction term (Win x Candidate elected in t-1)&quot;, y = &quot;Count&quot;) 
</code></pre>

<pre><code>## `stat_bin()` using `bins =
## 30`. Pick better value with
## `binwidth`.
</code></pre>

<p><img 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p6xhK4FVx8qx2+vzDnWf1ulBf/kk0/aYYcdZjfddFPSuItiG1eRvdzN+NFHHyWhanvDDTf0+xtssIFp4l0YZriLQGP6ffr0CQ/xCwEIVAGBxHbbNGjlSmUZ07rjRphaimqVJdZc41sSajB8MnPl/25d/XnFqTnrTHOtiW/jNbUloy9j5Yy7DzIGOrYqqIvbG23Xcle+pt6EKy41c93NCmoZJ0MbZ0SyvJzI+DQ4v6q7ObHH7lbrWs0J1wtSd/b5Vr/f95JZhhsJZ5STQYZR5ThZ1buRDGm9oMnjrgdDvR8+OFaJUBexPHCw2U47+mGJum12tPo/3rJSz+/u4FvEyTya2QjrJBuv+ht+b3UHHebrV0Murts2YxmJNVb/tjQZ5nBf2woz3dLESqv6kA6bbGyJE45bec4d18uADyGnlXtV9bfoBl7rQR911FH2q1/9ygYOHOjHz2e5SXIL1RXmgj6Z+/Wvf23qjtcM+5tvvtmOP/745LmxY8fahAkT3H2zxEaPHm3Dhg3zLwY+An8gAIHIEtA9rpZwMULi+26c+OV/W/2qlrjG3f249ddfewNYc8ThVn//A76oQOX+Y5xv2SXccVu8xGp+cKIljj9uZUveTc5rLqiVnfjeAX7MXHGDRx+3+uNPSiZLuK5qbwRXX31l3nvvacEDrrfQGc0mg0vjJh41Op1wLyDq6ve9Bk6f4D+v+Th+DNmNEftW9HPjXNf8xGQPQaNMUg4k9nKyuG7+YNo0C5TfPa4120xIHDHUtaL/a4nhx3p9NMxgX8734/zWvbvV3nGL6x53+k+d1uBFp1G2TemYhVf9vW6Y5KpfWu0lF1lCjbc5bvKeXqaayKtRmTqg60yte3ddqIWu+pbcfrgjY4JVB/MpI1s+FXKu6Ab+xhtv9MZc37lrMl34/9JL3duuC+eff76/sDfZZBPbfffdbciQIabJOQr6Zv7cc8+1PfbYw/q67+inuQv2uuuu8+f4AwEIRJuA7vVPPvmkKEKqFVvrxro1YWzFZv2tbpMt/YS3Gjdmq5A4dpjr1v1q5YS4rba3mp+e78diE27sVy1kPxlrt73Mtt3GEqu+6PEJs/yp+cl5FrjJXCsG7usnvtVcfkmD2DWjfuqM531+IlvdUcdZwhnAbN3via3csMFT//QT2xpktPNOlnAvCnUDdrC6vfd3Xfub+tOJgw/0LzQr9hpkdTvtbjUX/9wPKTRIm2FHMtRc+xur28+lP9Tpv64bIlDLNktIbNrPahzDuj328fqam2yYOOQg09i0egP0BYMmE6r3Qy8jTYUmdXQJmuJVc8ZpVv+/F/jJfvX/N9ps++18GdnyylS+PrGu+e3VVnf8D0zM6q+9fqWhzxR51bHEzt+1+p/93Or/8tcsseJzKuHeIovzyp0nE0240yz5dnqjSgvygqeJdWuEXSxp57Ptagxen+htvfXW2aJxDgIQKDIB3c+TJk3Ka0it/qFHrP7wo1ZK0ruXtfnvR42k8jOm3cNc4+fpQZPCfPds2G27KoJPo7HWtOPp6TPta/Z3IouB1OStRBfXVZ9D8OP9rnWqHoD04L/pd4Zehio1qEdCQwDJ7vTUkxm2xSAY6yYcuh4Lhfpf/NKPa+ulp7ngnf24IQJNmEsNgTyQuuGKXMaqs+moPDPx8mnE0bW6U0NzeaXGTd3OVEbq+dRtP28iRuPwde76uvjii+3yyy9PVdNvu0Gc8oQ111yzyYL17bz+EyAAAQg0GF9Ow5FumMLT2dKEcZr6zWbclSZX4+7j6gWjiZeM5BhxmiCJbN3+aXF9Gc441999j9W5YQq13OWcp/aJRzLEbHzIG/A04+7zzGXewqrs/EtUEzr6vDK8DPk0acbdx83Ca1VxGX/yqpMqsi1lM/AZa4mDEIAABCCQNwEZdO8LQLPRXbd1pt6OvDMlQcUTwMBXfBWiAASiQWD8+PG2jj4RI5SFQGLLLcpSLoVGlwAGPrp1g2QQqCgC22/vvk8mQAACkSFQ9Fn0kdEMQSAAAQhAAAJVTAADX8WVj+oQgAAEIBBfAhj4+NYtmkGgpATk6yLd1XRJBaAwCECgAQEMfAMc7EAAAoUS+M1vfmNfffVVoclJBwEIFJkABr7IQMkOAhCAAAQgEAUCGPgo1AIyQAACEIAABIpMAANfZKBkB4FqJaDFpfBAWa21j95RJMB38FGsFWSCQAUSePzxxytQakSGQHwJ0IKPb92iGQQgAAEIVDEBDHwVVz6qQwACEIBAfAlg4ONbt2gGAQhAAAJVTAADX8WVj+oQKCaB1d3a5v/973+LmSV5QQACLSCAgW8BPJJCAALfElixYsW3O2xBAAJlJ4CBL3sVIAAEIAABCECg+AQw8MVnSo4QgAAEIACBshPAwJe9ChAAAvEg8OCDD9raa68dD2XQAgIxIICjmxhUIipAIAoE9ttvvyiIgQwQgMAqArTguRQgAAEIQAACMSSAgY9hpaISBCAAAQhAAAPPNQABCBSFwA033GALFy4sSl5kAgEItJwABr7lDMkBAhBwBEaNGmXz58+HBQQgEBECGPiIVARiQAACEIAABIpJAANfTJrkBQEIQAACEIgIAQx8RCoCMSBQ6QT69+9vbdu2rXQ1kB8CsSHAd/CxqUoUgUB5Cbz88svlFYDSIQCBBgRowTfAwQ4EIAABCEAgHgQw8PGoR7SAAAQgAAEINCCAgW+Agx0IQAACEIBAPAhg4ONRj2gBgbIT2GijjWzmzJlllwMBIACBlQQw8FwJEIBAUQjMnTvX6uvri5IXmUAAAi0ngIFvOUNygAAEIAABCESOAAY+clWCQBCAAAQgAIGWE8DAt5whOUAAAo7ALbfcYmuuuSYsIACBiBDA0U1EKgIxIFDpBI466qhKVwH5IRArArTgY1WdKAMBCEAAAhBYSQADz5UAAQhAAAIQiCEBDHwMKxWVIFAOAn/5y19s8eLF5SiaMiEAgQwEGIPPAIVDEIBA/gROO+00GzhwoHXu3LlR4t///vf2zTffNDq+0TuT7JBVRxcuXGi3Xn11gzht2rSxkSNHWseOHRscZwcCEGieAAa+eUbEgAAEWkhgypQpGZ3gdJkzJ5lz3Yo6mz59enI/3FixYkW4yS8EIJAHAbro84BFVAhAoLQEEolEaQukNAjEiAAGPkaViSoQKCeBXr16WW1tbTlFoGwIQCCFAF30KTDYhAAECifw3nvvFZ6YlBCAQNEJ0IIvOlIyhAAEIAABCJSfAAa+/HWABBCAAAQgAIGiE8DAFx0pGUIAAhCAAATKTwADX/46QAIIxILADjvsYLNmzYqFLigBgTgQwMDHoRbRAQIRIPD+++8b36xHoCIQAQKrCGDguRQgAAEIQAACMSSAgY9hpaISBCAAAQhAAAPPNQABCBSFwFVXXWWrr756UfIiEwhAoOUEcHTTcobkAAEIOAKnnnoqHCAAgQgRoAUfocpAFAhAAAIQgECxCGDgi0WSfCAAAQhAAAIRIoCBj1BlIAoEKpnA3//+d/v6668rWQVkh0CsCGDgY1WdKAOB8hE4+uijbd68eeUTgJIhAIEGBDDwDXCwAwEIQAACEIgHAQx8POoRLSAAAQhAAAINCGDgG+BgBwIQKJRAp06dLJFIFJqcdBCAQJEJ8B18kYGSHQSqlcBnn31WraqjNwQiSYAWfCSrBaEgAAEIQAACLSOAgW8ZP1JDAAIQgAAEIkkAAx/JakEoCEAAAhCAQMsIYOBbxo/UEIDAKgKDBg0yxuG5HCAQHQIY+OjUBZJAoKIJvPLKK7Zs2bKK1gHhIRAnAhj4ONUmukAAAhCAAARWEcDAcylAAAIQgAAEYkgAAx/DSkUlCJSDwKhRo6x79+7lKJoyIQCBDARwdJMBCocgAIH8CVxwwQX5JyIFBCDQagQw8K2GlowhED8Ct99+e0FLwtbX18cPBhpBIOIEMPARryDEg0CUCLz11lu2fPnyKImELBCAQBMEGINvAgyHIQCB/AjMmjXL6urq8ktEbAhAoNUIYOBbDS0ZQ6C6CDzyyCO2ZMmS6lIabSEQYQIY+AhXDqJBAAIQgAAECiWAgS+UHOkgAAEIQAACESaAgY9w5SAaBCAAAQhAoFACzKIvlBzpIACBBgROPvlka9OGR0oDKOxAoIwEaMGXET5FQyBOBDDucapNdIkDAQx8HGoRHSAAAQhAAAJpBDDwaUDYhQAEIAABCMSBAAY+DrWIDhCIAIEnn3yyIDe2ERAdESAQSwIY+FhWK0pBoPQEPv74Y1uxYkXpC6ZECEAgIwEMfEYsHIQABCAAAQhUNgEMfGXXH9JDAAIQgAAEMhLAwGfEwkEIQCBfAv3797e2bdvmm4z4EIBAKxHAK0UrgSVbCFQbgd12263aVEZfCESaAC34SFcPwkEAAhCAAAQKI4CBL4wbqSAAAQhAAAKRJoCBj3T1IBwEKofAl19+afX19ZUjMJJCIOYEMPAxr2DUg0CpCNx77722ePHiUhVHORCAQDMEWnWSnZxejBs3zgYPHpwUIwgCe+edd5L72lhjjTWsV69eyWMzZ860yZMn24477mjdu3dPHmcDAhCAAAQgAIHcCLSqgT/rrLPsmWeeaWDgp0yZYttss42ttdZaSQmHDx9uV199td8fOXKkPfTQQ7bJJpuY4j711FO2/fbbJ+OyAQEIQAACEIBA8wRaxcCr9f3DH/7QJk2a5FvnqWK88cYbtssuu9iLL76YethvP/roo/b000/b1KlTrUuXLnbttdfa6aefbhMmTGgUlwMQgAAEIAABCDRNoFXG4O+55x47+OCDbcyYMY1Kfv31133XuybkyNgvX748GUet/aFDh3rjroOnnHKKTZw40WbNmpWMwwYEIBBNAuqJ69y5czSFQyoIVCGBVmnBjx492qN87LHHGiGVgf/ggw981/v06dOtT58+ppb7pptuatofOHBgMk3Xrl2tU6dO3sD37NnTH7/wwgttzpw5yTjpG+3atbNTTz01/TD7EIBAKxPAuLcyYLKHQJ4EWsXAZ5Nhu+22s5NOOsmGDRtm8+fPt6OPPtp3w6trft68eY1aADLwqTNzL7744qyf4mR6qcgmD+cgAAEIQAACcSRQcgN/5ZVXJjmuttpq3rjLyOv72R49etiCBQuS57Wh/d69eyePqYWeLbRpU3KVsonDOQhAoAUE9NXNDTfcYLW1tXnloufAmWeeae3bt88rHZEhECcCJbWG+mzusssu82Pr6ppX+Oqrr2yLLbawmpoa22CDDezDDz9M8p0xY4Y3/GHc5Ak2IACByBF49tln/QTaDh06FFW2Qufg4HSnqNVAZhVIoFUm2TXFQW/VmmEvI19XV2ezZ8/2E/GOPPJIn2TEiBE2duxYP2t+yZIlNnr0aN+VT6u8KaIch0B0COiz1tRJs9GRDEkgUJ0ESmrghfjyyy/3k+Q22mgj23jjjW399de3s88+29MfMGCAnXvuubbHHntY3759bdq0aXbddddVZ82gNQQgAAEIQKAFBFq1i16fyul/atBseTmyUde8WuaaRJcaRo0aZeedd56fWCcPdwQIQAACEIAABPIn0KoGPps43bp1a/K0JsYwOaZJPJyAQCQJ9OvXz7+0R1I4hIJAFRIom4GvQtaoDIFYE9hnn31irR/KQaDSCJR8DL7SACEvBCAAAQhAoBIJYOArsdaQGQIQgAAEINAMAQx8M4A4DQEI5EZAn7bKMQ0BAhCIBgEMfDTqASkgUPEE/vznP9uiRYsqXg8UgEBcCGDg41KT6AEBCEAAAhBIIYCBT4HBJgQgAAEIQCAuBDDwcalJ9IAABCAAAQikEMDAp8BgEwIQKJyA1pRI90xZeG6khAAEWkoARzctJUh6CEDAE8C1NBcCBKJFgBZ8tOoDaSAAAQhAAAJFIYCBLwpGMoEABCAAAQhEiwAGPlr1gTQQqFgCL730ki1durRi5UdwCMSNAAY+bjWKPhAoE4G3337bli1bVqbSKRYCEEgngIFPJ8I+BCAAAQhAIAYEMPAxqERUgAAEIAABCKQTwMCnE2EfAhAoiECfPn2sTRu+vC0IHokg0AoEuBtbASpZQqAaCRx44IHVqDY6QyCyBGjBR7ZqEAwCEIAABCBQOAEMfOHsSAkBCEAAAhCILAEMfGSrBsEgUFkE6uvrK0tgpIVAzAlg4GNewagHgVIRuPnmm23hwoWlKo5yIACBZghg4JsBxGkIQAACEIBAJRLAwFdirSEzBCAAAQhAoBkCGPhmAHEaAhCAAAQgUIkEMPCVWGvIDIEIEjjkkEOsU6dOEZQMkSBQnQRwdFOd9Y7WECg6gfXWW6/oeZIhBCBQOAFa8IWzIyUEIAABCEAgsgQw8JGtGgSDAAQgAAEIFE4AA184O1JCAAIpBF577TXWg0/hwSYEyk0AA1/uGqB8CMSEwMSJE23p0qUx0QY1IFD5BDDwlV+HaAABCEAAAhBoRAAD3wgJByAAAQhAAAKVTwADX/l1iAYQiASBHj16WG1tbSRkQQgIQMCM7+C5CiAAgaIQOPzww4uSD5lAAALFIUALvjgcyQUCEIAABCAQKQIY+EhVB8JAAAIQgAAEikMAA18cjuQCAQhAAAIQiBQBDHykqgNhIFC5BG699VZbtGhR5SqA5BCIGQEMfMwqFHUgUC4Cy5cvtyAIylU85UIAAmkEMPBpQNiFAAQgAAEIxIEABj4OtYgOEIAABCAAgTQCGPg0IOxCAAKFEdh///2tY8eOhSUmFQQgUHQCOLopOlIyhEB1Eujbt291Ko7WEIgoAVrwEa0YxIIABCAAAQi0hAAGviX0SAsBCEAAAhCIKAEMfEQrBrEgUGkE3n33XVu2bFmliY28EIgtAQx8bKsWxSBQWgIvvviiLV26tLSFUhoEINAkASbZNYmGExCIL4FLLrnEFi5cmLeCcmZDgAAEKoMABr4y6gkpIVBUAjLutLaLipTMIBA5AnTRR65KEAgClUmgW7duVlPDI6Uyaw+p40iAFnwcaxWdIFAGAsccc0wZSqVICECgKQK8bjdFhuMQgAAEIACBCiaAga/gykN0CEAAAhCAQFMEMPBNkeE4BCAAAQhAoIIJYOAruPIQHQJRIvCXv/zFFi1aFCWRkAUCVU0AA1/V1Y/yECgeARn3IAiKlyE5QQACLSKAgW8RPhJDAAIQgAAEokkAAx/NekEqCEAAAhCAQIsIYOBbhI/EEIBASGDgwIHWoUOHcJdfCECgzARwdFPmCqB4CMSFwBZbbBEXVdADArEgQAs+FtWIEhCAAAQgAIGGBDDwDXmwBwEIQAACEIgFAQx8LKoRJSBQfgIfffSRsZxs+esBCSAQEmAMPiTBLwQg0CIC48aNs2OPPdbatm3bonyKlfj111+3du3a5Z3ddtttx6p4eVMjQRQJYOCjWCvIBAEItJjA2LFjLZFI5JXP0qVLbeutty7oxSCvgogMgRIQwMCXADJFQAACpSewbNmy0hdKiRCIEAHG4CNUGYgCgUom0L59+7xbzJWsL7JDIOoEaMFHvYaQDwIVQuDEE0+sEEkREwLVQYAWfHXUM1pCAAIQgECVEcDAV1mFoy4EIAABCFQHAQx8ddQzWkIAAhCAQJURwMBXWYWjLgRai4A+S1u8eHFrZU++EIBAngQw8HkCIzoEIJCZwLx586y+vj7zSY5CAAIlJ4CBLzlyCoQABCAAAQi0PgEMfOszpgQIQAACEIBAyQlg4EuOnAIhEE8CO+20k8nZDQECEIgGARzdRKMekAICFU9gm222qXgdUAACcSJACz5OtYkuEIAABCAAgVUEshp4LdbwxBNPNII1bdo0e/DBBxsd5wAEIAABCEAAAtEgkLGL/vPPP7fp06fb/Pnz7ayzzrIePXokpQ2CwO69916bNGmSHX744cnjbEAAAtVN4JNPPrF1113X2rTJ+FipbjhoD4EyEMh4J8qIDx061BYsWGCLFi2y/fbbLylaTU2NrbXWWvaLX/wieYwNCEAAAo8//rgde+yx1rVrV2BAAAIRIJDRwMuAf/zxx/bVV1/Zj3/8Y7v99tsjICoiQAACEIAABCCQK4GMBj5M3K1bN7v11lvt+eeft7lz5zbwUtW7d2/bZZddwqj8QgACEIAABCAQIQJZDbzkHDx4sL300ku24YYbWrt27ZKiDxo0CAOfpMEGBCAAAQhAIFoEshr4qVOn2sSJE02/qRPtoqUC0kAAAlEgcOqpp0ZBDGSAAARWEcj6mZy8Uqmbfu211wYYBCAAAQhAAAIVRCCrge/Vq5fJ/eQf/vAHP6O+gvRCVAhAAAIQgEBVE8hq4PUd/AsvvGBnnnmmrbbaapZIJJL/jzrqqKoGh/IQgAAEIACBKBPIOgav7nkZ+EyhS5cumQ5zDAIQqFICjz32mO27777WsWPHKiWA2hCIFoGsBr6urs7ee++9jBKvt9563mtVxpMchAAEqo7AzJkzbcWKFVWnNwpDIKoEshr4xYsX2/nnn5+UXTev3FHqc7mf/vSntt122yXPZdpQ/HHjxvlP7dLPa3a+wrbbbtvItaUeFJMnT7Ydd9zRunfvnp6UfQhAAAIQgAAEmiGQ1cBr3D29BS+jfcIJJ1jfvn2bydq8H/tnnnmmgYGXn/udd97Zz87/7LPPrF+/fqauvc6dO/v8Ro4caQ899JBtsskmNmXKFHvqqads++23b7YsIkAAAhCAAAQg8C2BrJPsvo327ZYWkjj99NOzuq9V63vvvfe2+++//9uEq7bOOeccO+CAA+zVV1+1Dz/80E/a0yx9hUcffdSefvpp/929nOtceOGFvqxVSfmBAAQiTGDAgAENnGFFWFREg0BVEMjbwC9dutQb7k6dOjUJ6J577rGDDz7YxowZ0yiOWvTqAVDo0KGDHXfccfbwww/7fZ3TIjfhBL5TTjnFO9qZNWuWP88fCEAgugTUMyffGQQIQCAaBLJ20X/xxRfWs2fPpKRaZW758uW2xhprmFaOaiqMHj3an1LXe2rQ+vKffvqpaYJeGJT/7Nmz/a6WqB04cGB4yq9KpRcJGfhQji+//NI0+a+p8PXXXzd1iuMQgAAEIACBqiGQ1cBrgttrr73WAIbe0Pv06VPQm7rG3/WSELbQlbEMuJakVZg3b15yLN4fcH90XpP9wnDjjTf6hW/C/fRf5f+d73wn/TD7EIAABCAAgaoikNXA19bWFtVYyuWtnOXIgY4m8CloWyvTKcjfvdagTw3aD8/r+KhRo1JPN9rWBD0CBCBQegJacVK9e3puECAAgfITyGkM/q9//asdf/zxfnz8kksuaTSzPlc1NEFP7m8/+uijZBJta6U6hQ022MBPvPM77s+MGTP8ErXqMSBAAALRJvDAAw/YkiVLoi0k0kGgigg0a+DPO+88++EPf+gdWKjr+4knnvDj5G+//XZBmEaMGGG//vWvfXe8PoO7+eab/cuDMtO5sWPH2oQJE/yDQmP5w4YNa/SdfEEFkwgCEIAABCBQRQSyGnh1n19//fV+Jrta8Zdddpm98sordtJJJ5nGwgsJcpyjcXJ957777rvbkCFD7NBDD/VZ6TObc8891/bYYw//nf20adPsuuuuK6QY0kAAAhCAAASqmkDWMfg5c+Z4RzQyxqnhkEMOsZ/97GephzJu61M5/U8NGnv/xz/+YZpw17Vr10bfzWqMXb0Gmlin8TwCBCAAAQhAAAL5E8jagt900019a/vZZ59N5ixPdjfddFOLJ9+tueaajYx7WIhm6mPcQxr8QqAyCGiILfULmcqQGikhEF8CWVvwUls+5/fff3/fkpfBHz9+vG95q6ueAAEIQCAkIMdVBAhAIDoEmjXwJ554ol9URl7m1GWvMfOjjz660ffq0VEJSSAAAQhAAAIQaLKLvr6+3n73u995r3Fbb721nX322bbDDjv42fSs98yFAwEIQAACEIg2gYwGXs5l5DL2V7/6lcldbRg0Nn7RRRf51eAWLlwYHuYXAhCAgF8aGlfRXAgQiA6BjAY+9CE/adIkk/e5MBx00EHeEY1a99l80Yfx+YUABKqHgJxWaRIuAQIQiAaBjAZey7ZqxTd9xpYeNEs2dEiTfo59CEAAAhCAAASiQSCjgZcjGv1vKqhVr5XhCBCAAAQgAAEIRJNARgO/5557JtdozyT2/fffb3vttVemUxyDAASqlMDmm29ubdu2rVLtURsC0SOQ0cAfeeSRNt2tzX744Yf7hWW0/rpa7PI/L9/wL730kh122GHR0waJIACBshFQw4Bv4cuGn4Ih0IhARgOvLvjnnnvOz6Dv37+/X5Nd67LLV/zs2bPtP//5j/cV3yg3DkAAAhCAAAQgEAkCTTq6CY38okWL/GIzWuN52223zTjxLhKaIAQEIAABCEAAAkkCTRr4MIZmze+9997hLr8QgAAEMhKQb4zOnTtbTU3GjsGMaTgIAQi0HgHuxNZjS84QqCoCd999t18FsqqURlkIRJgABj7ClYNoEIAABCAAgUIJYOALJUc6CEAAAhCAQIQJYOAjXDmIBgEIQAACECiUAAa+UHKkgwAEGhCQjwxNsiNAAALRINDsLPpoiIkUEIBA1Al079496iIiHwSqigAt+KqqbpSFAAQgAIFqIYCBr5aaRk8IQAACEKgqAhj4qqpulIVA6xF44YUX7Jtvvmm9AsgZAhDIiwAGPi9cRIYABJoiMGnSJFu+fHlTpzkOAQiUmAAGvsTAKQ4CEIAABCBQCgIY+FJQpgwIQAACEIBAiQlg4EsMnOIgEFcCffv2tTZt+PI2rvWLXpVHgLux8uoMiSEQSQL7779/JOVCKAhUKwFa8NVa8+gNAQhAAAKxJoCBj3X1ohwEIAABCFQrAQx8tdY8ekOgyASWLVtmQRAUOVeygwAECiWAgS+UHOkgAIEGBG677TZbtGhRg2PsQAAC5SOAgS8fe0qGAAQgAAEItBoBDHyroSVjCEAAAhCAQPkIYODLx56SIQABCEAAAq1GAAPfamjJGALVReCwww6zTp06VZfSaAuBCBPA0U2EKwfRIFBJBNZZZ51KEhdZIRB7ArTgY1/FKAgBCEAAAtVIgBZ8NdY6OkMAAk0SuOKKK5o819QJ+eAfNWqU1dTQZmqKEcdLTwADX3rmlAiBWBKYMGGCDRgwwNq3b1/R+n3++ecVLT/CQyAkwOtmSIJfCECgRQRef/11kzc7AgQgEA0CGPho1ANSQAACEIAABIpKAANfVJxkBgEIQAACEIgGAQx8NOoBKSBQ8QR69uxptbW1Fa8HCkAgLgSYZBeXmkQPCJSZwJAhQ8osAcVDAAKpBGjBp9JgGwIQgAAEIBATAhj4mFQkakAAAhCAAARSCWDgU2mwDQEIQAACEIgJAQx8TCoSNSBQbgI333yzLVy4sNxiUD4EILCKAAaeSwECECgKgfr6+qLkQyYQgEBxCGDgi8ORXCAAAQhAAAKRIoCBj1R1IAwEIAABCECgOAQw8MXhSC4QqHoC3/ve96xjx45VzwEAEIgKARzdRKUmkAMCFU5g/fXXr3ANEB8C8SKAgY9XfaJNlRGYNm2arVixIm+tmRCXNzISQKDiCGDgK67KEBgC3xIYM2aMLV++3BKJxLcHc9hSGgIEIBBvAhj4eNcv2sWcgFriUVmD/a233rLNN9/c2rVrF3PqqAeByiDAJLvKqCekhEDkCbz88su2dOnSyMuJgBCoFgIY+GqpafSEAAQgAIGqIoCBr6rqRlkIQAACEKgWAhj4aqlp9IRAKxNYY401rKaGR0orYyZ7CORMgEl2OaMiIgQgkI3AkUceme005yAAgRIT4HW7xMApDgIQgAAEIFAKAhj4UlCmDAhAAAIQgECJCWDgSwyc4iAAAQhAAAKlIICBLwVlyoBAFRC48847bdGiRVWgKSpCoDIIYOAro56QEgKRJ/D1119bEASRlxMBIVAtBDDw1VLT6AkBCEAAAlVFAANfVdWNshCAAAQgUC0EMPDVUtPoCYFWJrDPPvtYhw4dWrkUsocABHIlgKObXEkRDwIQyEqgX79+Wc9zEgIQKC0BWvCl5U1pEIAABCAAgZIQwMCXBDOFQAACEIAABEpLAANfWt6UBoHYEnj//fdt+fLlsdUPxSBQaQQw8JVWY8gLgYgSGD9+vH3zzTcRlQ6xIFB9BDDw1VfnaAwBCEAAAlVAAANfBZWMihCAAAQgUH0EMPDVV+doDIFWIdC5c2dLJBKtkjeZQgAC+RPgO/j8mZECAhDIQGD48OEZjnIIAhAoFwFa8OUiT7kQgAAEIACBViSAgW9FuGQNAQhAAAIQKBcBDHy5yFMuBCAAAQhAoBUJYOBbES5ZQ6CaCNxzzz22ePHialIZXSEQaQIY+EhXD8JBoHIIzJ8/3+rr6ytHYCSFQMwJYOBjXsGoBwEIQAAC1UkAA1+d9Y7WEIAABCAQcwIY+JhXMOpBoFQEdt11V2vfvn2piqMcCECgGQI4umkGEKchAIHcCGy11Va5RSQWBCBQEgK04EuCmUIgAAEIQAACpSWAgS8tb0qDAAQgAAEIlIQABr4kmCkEAvEn8PHHH9uKFSvirygaQqBCCGDgK6SiEBMCUSfw5JNP2tdffx11MZEPAlVDAANfNVWNohCAAAQgUE0EMPDVVNvoCgEIQAACVUOg5J/JBUFg77zzTgPAa6yxhvXq1St5bObMmTZ58mTbcccdrXv37snjbEAAAtElUFtba4lEIroCIhkEqoxAyQ38lClTbJtttrG11loriXr48OF29dVX+/2RI0faQw89ZJtssokp7lNPPWXbb799Mi4bEIBANAmccsop0RQMqSBQpQRKbuDfeOMN22WXXezFF19shPzRRx+1p59+2qZOnWpdunSxa6+91k4//XSbMGFCo7gcgAAEIAABCECgaQIlH4N//fXXfdf7l19+aTL2y5cvT0r3zDPP2NChQ71x10G1CCZOnGizZs1KxmEDAhCAAAQgAIHmCZS8BS8D/8EHH/iu9+nTp1ufPn1MLfdNN93UtD9w4MCk1F27drVOnTp5A9+zZ09//Morr7R58+Yl46RvaLnKjTbaKP0w+xCAAAQgAIGqIlByA7/ddtvZSSedZMOGDTOtH3300Uf7bnh1zctwd+7cuUEFyMAvXrw4eezUU0/N6kxj3LhxybhsQAACpSPw8MMP26BBg/xLeelKpSQIQKApAiU38GqBh2G11Vbzxl1GXi3vHj162IIFC8LT/lf7vXv3Th7TjPtsQS8EBAhAoPQEZs+ebXV1daUvOCIl/u53v8tbkrZt2/pnYN4JSQCBHAiU1MDLjeVll13mx9bVNa/w1Vdf2RZbbGE1NTW2wQYb2IcffpgUe8aMGd7wh3GTJ9iAAAQgEDECGnokQCBKBEo6ya5Nmzb++3YZeb3p641/zJgxduSRR3omI0aMsLFjx/pZ80uWLLHRo0f7rnylI0AAAhCAAAQgkDuBkhp4iXX55ZfbnDlz/ES4jTfe2NZff307++yzvcQDBgywc8891/bYYw/r27evTZs2za677rrctSEmBCBQNgLyV9G+ffuylU/BEIBAQwIlbxprtrwc2ahrXi3z9DHzUaNG2Xnnnecn1jU33t5QFfYgAIFyEthhhx3KWTxlQwACaQRKbuDD8rt16xZuNvpVK4CWQCMsHIAABCAAAQjkTKDkXfQ5S0ZECEAAAhCAAAQKJoCBLxgdCSEAgVQC1f6ZXCoLtiEQBQIY+CjUAjJAIAYE5OhGX78QIACBaBDAwEejHpACAhCAAAQgUFQCGPii4iQzCEAAAhCAQDQIYOCjUQ9IAQEIQAACECgqAQx8UXGSGQSql8DJJ5+cXOq5NSgkEglbe+21Tb/5Bn2Wq7Uv9PntOuus06Sc6X458i2H+BCIEgEMfJRqA1kgUMEE5LiqEOObi8qbb765/ehHP7Ldd9/dzjjjDL+8dC7pFGettdYyrULZq1cvO+uss0x5HXDAARmT77333tauXbuM59IP1tbW2iGHHJJ+mH0IRIZA2RzdRIYAgkAAApEmoBXXDj30ULvmmmts+fLl3vvlvvvua1OmTLGOHTvaNttsY1988YW9//77Xo8uXbrYtttua1r8RZ/u7bbbbrZw4UJTK14t+E8//dS+/PLLZFy52NXS1W+99ZZ3j62VLRU222wzn+b111/3S1RvueWW3gNnv3797OWXX/butPv37286r5cCeemcPn26vffeez49fyBQbgK04MtdA5QPAQhkJaDVJD/66CNv3BVRn+I9+uijPs2wYcO8cV9vvfVs8ODBppeBE0880RvaffbZx69QKYM+d+5cmzp1qneBLaO/1157+fQaVpg1a5bv+t9uu+1s11139XkoLy1fLZfaxx57rI+rXw0RLFu2zA488EC/psaiRYv8y4Na8q+99ppttdVW/qXAJ+APBMpMAANf5gqgeAjEhcCTTz5pX3/9ddHVUatdhjs1qMWsFrlWpVTLffz48b7FraWn1UrXglVqyW+99da2YMECkyHWIlcyzmrNKyi95FVPwLhx4+zVV19NFiFDrYWw1LrXMtYdOnTwxl6tdbX0lVb5LF261OevMoYOHep7B8L8k5mxAYEyEaCLvkzgKRYCcSPw8ccf+67sYuulFnbv3r1917xa75osp1b67373u+RiVTLAGv9XV7sMtlr4WqxKk+aamjgno69xdAXlKUMeBnX5P/DAA/4FQN3wMuTffPNNeLrBr14oXnzxRd8TMGTIEB8v9WWhQWR2IFBCAhj4EsKmKAhAIH8CK1as8MZWRl0tbhnUsWPH+pcJtd7VdS5jrh6EGTNm2E477WRHH320N9r33XdfkwZeY+3vvvuuHXPMMT7OY489lhTuueees6OOOsobdrXI33nnneS5cEO9Aauvvrqp10Atfr0AaH+6G4cnQCAKBBKBC1EQpFgyaCnajTbayHfNFStP8oFAVAn85Cc/aZVu8UL0HTNmjDe2Xbt2zTl5/6nT7eS//9PHn9+5s11y0srx7qYyUFe9uuxTg1rhMtapjzLN6NeLQS6hpqbGp88Ut7l8wq8GVHYm2TLlmX7shhtuSD/EPgRyJqBhqosvvtguv/zyRmlowTdCwgEIQKAQAurKTh8rLySfbGnSjbvi6gGXHnI17koXzppPz0P7zeWT+lKRSbZMeXIMAqUigIEvFWnKgUDMCehzNAIEIBAdAsyij05dIAkEIAABCECgaAQw8EVDSUYQgAAEIACB6BDAwEenLpAEAhVNQN7hso1nR0k5TazTp3UECMSZAAY+zrWLbhAoIYF7773Xe4orYZEFF6XP6r773e8WnJ6EEKgEAkyyq4RaQkYIQCBnAvJgJzezck2rX33Kpt6FXXbZxdRy//e//+3d3c6cOdM6u0/ztBhN9+7dfYt+woQJvhx5wNP39p9//rn3Zy8HOwQIVBoBWvCVVmPICwEIZCUgZzMy0AryTte3b1/bf//9k0Z94MCB3gmOFqmRB7sRI0Z4V7abbLKJd1ijlr0WjpE3u5NOOskb/6wFchICESVACz6iFYNYEIBA8QjI451WpJOXObmVVUs+DB9++KFv7cvYy8e8VouTq1u13idNmhRG4xcCFUfg26u84kRHYAhAIEoEjjvuON/lXW6ZNNEvdLgjg60ghzTyXa8V30444YQGIsrPfGrQanNrrrmmPxT+pp5nGwKVQoAWfKXUFHJCIOIENPYdhfDZZ5+Zlpg97LDD/Hrx06ZN84vK6AVEvuyba5VrrXctB6vlYzVG35w3uyjojAwQyEQAA5+JCscgAIGKJSCDfM0111i6H3l106f6i9eCNQrhr1r3ChqLf/rpp/3ysmeeeaafoOdP8AcCFUYAA19hFYa4EIBAbgQytbxz8RevLvrBgwf7WfXPP/88LfjccBMrggQw8BGsFESCQCUSGD9+vO28884V70BG68Tff//9lVgFyAyBBgSYZNcABzsQgEChBNQFnksLudD8SQcBCORHAAOfHy9iQwACEIAABCqCAAa+IqoJISEAAQhAAAL5EcDA58eL2BCAQBMENPtcM9ejHjSTPvxOPh9Z27VrVxH65aMTceNNAAMf7/pFOwiUjMC+++7rvzsvWYEFFiQXtfrGPd+w++6722abbZZvMuJDoGwEov+6XTY0FAyB0hF4++23Ld2jWi6l19XV5RItdnF69eplXbt2Nfmdf/XVV/3KcB07drS33nrL5Ohmyy23tK+++sq7nZXjmm+++cbWX399v69ehvnz53smG264oXeK88Ybb9jChQt9OsXVcS0807t3b7/ozJtvvunja+GaAQMGWLgvn/cqkwCBKBLAwEexVpCp6gj89a9/9UYo1Ud6LhCWLVuWS7TYxdFiMBtttJHpO/VDDjnE3nvvPdPqcMcff7xdddVVduyxx3oHNuIjr3QvvPCCj/fYY4/Z4YcfbjL6Sr/TTjvZSy+9ZD/4wQ/sxhtvtF133dU+/vhj/wJw/vnn21133eU//dNLg4Jc3qolry8G9IKxxRZbYOBjd3XFRyEMfHzqEk0qmIAMR6V/YiY3sB06dPDLs5aiKuR57oMPPvCrvmkFOC0WoyVeFdR6f/31170R/v73v+9b7tqX8Q6XhN1xxx1t3LhxNnfuXFMLXq1+hVdeecXXhc5rIRq14tdee21/Tn/UY7Dtttua1pTX0rMECESVAGPwUa0Z5IJAhRG48847/bKrpRI7fCHScq8y0Pfcc0+yaHWzpwY5r1F3voJa3gpaIz48JmMdDpGEHvDCX718pQa9WHznO9/xhl+r0xEgEFUCtOCjWjPIBQEI5ERgxowZtttuu/kZ7lpJrra2tlG6d99913bYYQcbNmyYaVEcjcFPnDjRhgwZ4sfvlUa9AcqnuaBu/yVLlviWf3NxOQ+BchLAwJeTPmVDAAIFEXj22WeT6eRWNnURGZ24/vrr/XlNnLv99tv99q233uqNf+rExDvuuKPBojR/+tOffFz9+eMf/+i3n3vuueSxcENzJeieD2nwG1UCdNFHtWaQCwIQyJlA2F3fXIJU4x7GDbviw/3mfrUMrcbyNc5PgECUCdCCj3LtIBsEKojAEUccYZ06daogiQsT9aGHHiosIakgUGICGPgSA6c4CMSVwJprrhlX1dALAhVJgC76iqw2hIYABCAAAQhkJ4CBz86HsxCAAARyIlCoj/ucMicSBAoggIEvABpJIACBxgTkHS78lrzx2WgfWW+99fzncpmk1Cd08pbXXCjUx31z+XIeAoUSYAy+UHKkgwAEGhCQT/b+/fsnvck1ONnCHfmel695+YifPHmyd0urLLX4S7du3bzXOs2Glzc6zQXQLHet/qZ08+bNs6lTp/rv4OXzX9/Ahz7kFadv374+nx49etikSZN8Onmq04x7fQqnMqWXPOHNnj3be7HTDHq5q1XI5OPen+APBMpMgBZ8mSuA4iEAgeYJaCna/fff3xt3+ZKXW9rBgwebjLKMrXzPK+hXXuz0MnD00UebPM0NGjTIDjjgAPv0009Nn7gp7LXXXv5Xs/7l5nbOnDn+v9IeeeSRvhwZ+IMOOsgflye8L774wk488UTvAU/l7rffft6FrVr3cmkrv/QECESJAC34KNUGskAAAk0S0BCAFpSRVzotFLPVVlt5o92nTx/bYIMNvB98uZ+Vhzq13GV01ZKXpzu5spWxl8HOFOQQRy8F6gWQf3oZ/e7du/uoOqehB60kJ7/0OqfttdZay3u0S/dxnyl/jkGgHAQw8OWgTpkQiCEBGVotxdpaQV3xCvIb/8knn/gW9QMPPGBa5EZd6DLCqQ5vQgc28iWfuq08Qne2YZ46JqMtP/Vqkcvrnc6plR4Gte7VRX/33Xd7PbWinfSV3gpKGy5DG6bhFwLlJNB6d2M5taJsCECg5AS0LGtrBq3upvFuGWy1zvV71FFHecOuVvY777yTc/Hqkh8+fLhfO16JNE5/8MEH+655+Zrfd999vftbdeFrX4vSqMdAY/THHHOMb+2rpyCTj/uchSAiBFqZQMLdJA2XSmrlAls7e3mZUvedJtEQIFApBC644IKSrsQWFS79p063k//+Ty/O/M6d7ZKTVo6lp8u35557+rFvTbBLbaUrnlrRYQs9PV22/fR08i+vxWoUNPlOhj0Mat0r6HGpePpNfXSqRyCTG9wwfbbfG264IdtpzkEgKwFddxdffLFdfvnljeLRgm+EhAMQgEDUCHz++eemVnq6cZechRj3TOlC465zqcZd+6nGPDWezikUatxXpuYvBFqHAAa+dbiSKwSqjoAMn1q3rRHy6X5vjfLJEwKVSKB17sZKJIHMEIBAiwjcfPPNvpXdokxIDAEIFI0ALfiioSQjCEAAAvkTCNedzydl+/btbcSIEfkkIW4VEsDAV2GlozIEIBAdAvKul2/QUAgGPl9q1RefLvrqq3M0hkBFEsjVJ3wm5bL5ms8UP9uxYuaVrRzOQaClBGjBt5Qg6SEAAU9ADmL03XhrBTmWCX3CywkWDokAAAAdu0lEQVSNWrGaVf/RRx818g8v//L9+vXzjmf0vfoOO+zg3drqO3Z9Sy+vdfov73jbb7+9d2Ajj3f6dE5+6ENf853dp3vyWCevdh06dLAJEyY0yEsuayWXvOS99957raU6+UKgIAK04AvCRiIIQCCdgFq2oYe49HPF2JdzmtAn/G677ebdxmo/3T+8nNLou/k333zTG3n5xEj1NR/6q+/Zs6eNHDnSG3f5t9e37ul5yee9usJVjvzhy9lNmJe+GtBLzWuvveaPp3rFK4a+5AGBlhKgBd9SgqSHAARKQiD0Cb9kyRJvjF988UX/fXq6f3j5kte49sCBA71bW7XCFyxYkPQ1L3/18kCnHoB1113X+6pXnnoxSM9LjnXkNU+r0cnYy4jLHa1a/0qjfIcOHerLk3wECESJAAY+SrWBLBCAQM4E1D0vJzfp/uG1Lru66B955BHbdddd/cuAMg290YXOcuS8JnSSo235sk/PS+kyrXGvvDSTXS8Zs2bNsiFDhvhV7F599VUlIUAgEgTooo9ENSAEBCqfgLqq0z3AFVOr0Ce8DHgY5EEu9A9/wgkn+CECGdx11lnH5Btfbqu1CIx8zcuXvVrsTYVMeWWKG+alFv8uu+ziy9G2xuEJEIgSAXzRR6k2kKVqCcTBF/2YMWP8euwyqLmGXH3Rh/mFrfBU17E6p+52HQuPK57mA4Qt9DBOJjezYd7hb3pe4fHUX8UJ82rbtm1GF7qp8Yu9rfJ/+9vfFjtb8qtAAviir8BKQ2QIQKAxgdCAp58JjW14XPFSjbuOp8cJ46b/5hIvNU7Y5Z+eD/sQKDcBuujLXQOUDwEIQAACEGgFAhj4VoBKlhCoRgL6Jrw1P5OrRqboDIGWEGAWfUvokRYCEEgS0LfkBAhAIDoEaMFHpy6QBAIQgAAEIFA0Ahj4oqEkIwhAAAIQgEB0CGDgo1MXSAIBCEAAAhAoGgEMfNFQkhEEqpvAbbfd5n22VzcFtIdAdAhg4KNTF0gCgYomIE9zTX2nXtGKITwEKpQABr5CKw6xIQABCEAAAtkIYOCz0eEcBCAAAQhAoEIJYOArtOIQGwJRIzBo0CC/jGrU5EIeCFQrARzdVGvNo3erELjiiits7ty5eeed7jc97wwikEArtxEgAIHoEMDAR6cukCQGBBYvXtxokZMYqIUKEIBABRLAwFdgpSEyBCBQ3QS0mp2WGM43dOzY0S666KJ8kxG/Qglg4Cu04hAbAlEjMGnSJNtkk02sXbt2URMtlvIsWrQob72++eabvNOQoHIJMMmucusOySEQKQIvvPCCLV26NFIyIQwEqpkABr6aax/dIQABCEAgtgQw8LGtWhSDAAQgAIFqJoCBr+baR3cIFJFAt27drKaGR0oRkZIVBFpEgEl2LcJHYghAICRwzDHHhJv8QgACESDA63YEKgERIAABCEAAAsUmgIEvNlHygwAEIAABCESAAAY+ApWACBCAAAQgAIFiE2AMvthEyS8WBJ577jn76quv8talmr8Dv/vuu23IkCHWpUuXvLmRAAIQKD4BDHzxmZJjDAg8/fTT9uWXX8ZAk9KpsHDhQguCoHQFUhIEIJCVAF30WfFwEgIQgAAEIFCZBDDwlVlvSA0BCEAAAhDISiCSBn7mzJk2btw4W7BgQVbhOQkBCESHwMCBA61Dhw7REQhJIFDlBCJn4EeOHGnbbrutX9Jw4403tldffbXKqwj1IVAZBLbYYgtr27ZtZQiLlBCoAgKRmmT36KOPmiY3TZ061c/Evfbaa+3000+3CRMmVEFVoGJrEPjggw+skCUyly9f3hrikCcEykpA68i//fbbectQW1trW265Zd7pSFBeApEy8M8884wNHTo0+ZnNKaecYueee67NmjXLevbsWV5SlF6RBP72t7/52fD5+kiv5s/dKrKiETonAjLwd9xxR05xw0j6MiKRSNhVV10VHuK3QghEysBPnz7dNI4Xhq5du1qnTp0aGHi1rLJ9ilNXVxcmL8qv8pNcpQq6kbLpV2w5Ci2v0HSFyi8DrYdTvkHXC63xfKkVFv+jjz6y9ddfn276wvCVLFUhL68aelH95hsKvW9Lna7Q51mh6dZbbz3r2LFjvjjzjh8pAz9v3jzr3LlzAyVk4BcvXpw8dvHFF9tnn32W3E/faN++vfXr1y/9cMH76t5VKzDfILmXLFmSbzKvf6q+uWaglyF9h5xv6N69e0GTGVdbbTWbP39+vsXZ6quvXtD35WussYZ98cUXeZenBCoz37Bo0SI/YaxNm/xuEaXTjasuzXyC6k7XTCnT6V7Lt2dDzn/kyCZTOk2M1ZCarqn00FS6Lp3nJqPW1CQa1ZXS6drWgzTXoBdkpdPqdqVMl0nvbDLrhVXXi+TMJ5Q6nWRTQ6eQ5+C6665rs2fPzkc9H3edddbJ+pxvKsMePXrYnDlzmjrd5PG1117b5s799lpsMmLaiULTDR8+3DbYYIO03Iq/m9/Tq/jlN8hRlZM+c177vXv3Tsa74oorktuZNh566KFMhws+pofghRdeWHB6EkKgWghozsz5559vffr0yVnl+ocesfr7Vt6z3dyLwaWXXppzWiJCAALZCURqFr3eaD788MOkxDNmzPDdsvk8MJKJ2YAABCAAAQhUMYFIGfgRI0bY2LFj/ax5dW+PHj3ahg0bZvl2k1ZxfaI6BMpGQEMhmbruyyYQBUOgyglEqot+wIABftb8HnvsYRrj1WcZ9913X5VXEepDoDIIqMeNAAEIRIdApFrwwjJq1Cg/QWby5Mn27LPP2lprrRUdWkgCAQhAAAIQqBACkWrBh8w0E17/CRCAAAQgAAEIFEYgci34wtQgFQQgAAEIQAACqQQw8Kk02IYABAomoLkzhXzzXHCBJIQABLISwMBnxcNJCEAgVwJvvPEGXgNzhUU8CJSAAAa+BJApAgIQgAAEIFBqAhj4UhOnPAhAAAIQgEAJCGDgSwCZIiBQDQTkZlb+KwgQgEA0CETyM7looEEKCEAgHwLnnHNOPtGJCwEItDIBWvCtDJjsIQABCEAAAuUggIEvB3XKhAAEIAABCLQyAQx8KwMmewhUC4Hx48fb0qVLq0Vd9IRA5Alg4CNfRQgIgcogcMghh9icOXMqQ1ikhEAVEMDAV0EloyIEIAABCFQfAQx89dU5GkMAAhCAQBUQiOVncr/97W9t7bXXroLqQ0UIRIeAxt+vvPJK69atW85CBVM+sMBWrIy/4HOr+dnPck5LRAhAwCwIAmvbtm1GFAl3Msh4hoMlJzB8+HC76667Sl5utRQI39atafi2Lt8f/OAHNmbMmCYf5q1bevxzHz16tB1//PG28cYbx0ZZuuhjU5UoAgEIQAACEPiWAAb+WxZsQQACEIAABGJDAAMfm6pEEQhAAAIQgMC3BDDw37JgCwIQgAAEIBAbAhj42FQlikAAAhCAAAS+JYCB/5YFWxCAAAQgAIHYEMDAx6YqUQQCEIAABCDwLYFa9+3f6G932So3gS222KLcIsS6fPi2bvXCt3X5br755pZIJFq3kCrOvW/fvtaxY8fYEMDRTWyqEkUgAAEIQAAC3xKgi/5bFmxBAAIQgAAEYkMAAx+xqvzss8/s+eeft7q6uohJFh9xVqxYYU8++WR8FIqAJjNnzrRx48bZggULIiBNPEXgum29etU6Cs8++6zNnz+/9QopQ84Y+DJAz1Skbt7999/fdt11V/vVr35lG220EX7pM4EqwrGzzjrLzjnnnCLkRBYiMHLkSNt2223toosu8n68X331VcC0AgGu21aA6rK86qqrTGPv1157rQ0YMMBGjBjROgWVIddYriZXBo4tLvLuu+/2b48ffvihn0TzzDPP2AknnGBHH300i0u0mO7KDCZPnmw//OEPbdKkSbbGGmsUKdfqzubRRx+1p59+2qZOnWpdunTxD8nTTz/dJkyYUN1giqg9120RYaZlNW/ePLvkkkvszTff9I0qteTVuFIv6sCBA9NiV94uLfiI1NnWW29tf/rTn5IzZPVGqW7PWbNmRUTCyhfjnnvusYMPPtivyFX52kRDA72IDh061Bt3SXTKKafYxIkTuW6LWD1ct0WEmZZVfX293Xfffd6o61T79u1tvfXW89dwWtSK3KUFH5Fq22abbRpIct1119mOO+5o66+/foPj7BROIPwi9LHHHis8E1I2IDB9+vQGLZ2uXbtap06dvIHv2bNng7jsFEaA67Ywbrmk6tGjhw0ePDgZ9V//+pe99dZbsRkexcAnqzY6GzLu6rJXNxEBAlEmoC7Ozp07NxBRBn7x4sUNjrEDgagTUDe9eqOuueYa22yzzaIubk7y0UWfE6biRho/frzV1NQk/2v2ZhguuOAC+81vfmPPPfec4TQkpJL/r97MQ8ZnnHFG/hmQIicC4pw+c177vXv3zik9kSAQBQJ6Ju+111526aWXWpyeF7Tgy3B1qetdb4th0KSOIAjsxz/+sT311FP24osv+lmd4Xl+8yegFyR9maCw5ppr5p8BKXIisMEGG5gmhoZhxowZpnHNPn36hIf4hUCkCeiT2WHDhvm5OZrUHKeAgS9DbapLc6uttmpQ8m233WZ/+9vf/LfEHTp0SE5SWmuttZhF34BUbjv0fuTGqaWx9EnRvvvuayeffLL179/fNF6sh2WbNjxaWsqW9K1P4NNPP7WjjjrK95pq1nw4qVlfhGg+SaUHuugjUoP69l3jmZpsp1mc4f/XX389IhIiBgQaE9B3w+eee67tsccevtdp2rRppjkkBAhUAoEbb7zRFi5caKeddlrymatnr7rq4xDwRR+HWkQHCJSZgL4f1sQ6/AuUuSIoHgIpBDDwKTDYhAAEIAABCMSFAF30calJ9IAABCAAAQikEMDAp8BgEwIQgAAEIBAXAhj4CNbk3Llz7f7777exY8faokWLiiah8tJMfU0s0Wp1+v7+hhtusHfffdceeeSRZsuR4x3NOi1G0GeBhYRQZvk+Tw36VOs///lP6qEmtwstu8kMczgRlrlkyZKcWOeQZaMoYRmNTrTygddee817/9L18dFHHzUo7Y033rC///3vDY5prF7X27Jly7wzp2JdUw0KSdtprXsqrRiT6945c+b4w9nul9R46Xmk77dGveo+mj17dnpRee8XIttXX33V6JoIC87nPg7ThL+5yvLCCy808N2gZ+LXX38dZhOvXweFEBEC7vvh4H/+538C95lcsN9++wWDBg0K3Kcawc9//vOiSHjMMccE/fr1C9yCK8GDDz4YuM/1guHDhwfuASxrG6j8bGG77bYLnF/sbFFyOnfssccGzhjnFDc1UqrMzp1k6qngyiuvDI444ogGxzLtuBeDwLmmzHSq1Y6llukMYE6s8xWmUKb5lpMe3/kaCJyDkMAtsxm4T+YC97lcgyhulbnAORwK3IM7efyhhx4KnBtbv1+sayqZedpGa99TacUFzgNa8PjjjzerW2q89DxS91OvndTjLd3ecsstg4cffrhF2RR6zenedd4OM5ad632cnvgvf/lL4BaNST/caP+f//xnUFtbG6Q+P+69997gsssuaxQ3DgfkYIUQEQIyvLrxv/zyy6REbuWzoGPHjsE//vGP5LFCN2Tc9XBVGDVqVKAbtBxh9dVXL8jAF0Nm3eBuIZ+Sql2KMgtl2lIQrjcouPzyy302f/7zn4PNN988maX7pjhws+qDww8/PHA9Rcnjbqle/zKQPNCKG619T6WLnqvhzjVea107xTDwhV5z2Qx8Os9c992SxcFPfvKTJqO73svgvPPOC9Zdd13/wplq4JXou9/9buAW92oyfaWewMBHpOaWL1/uDbnrPmokkXOjGLz33nv++Oeff+7fNvUgdd9rBueff37wzTffJNPccsstwaabbhp07949OPDAAwO3GIg/t/feewdt27YNnIcx3zOgB6/iuPXnA+d9zL9YhC1412UfHHDAAYHzAOfzcF2NPg/np9m39rXjvh0NdFOts846Xo6f/vSngety9fFuuumm4P/+7/8C9RisttpqwXe+851AOijomFp0zgNa4JYa9cdS/7ju3sCtSBasvfbaPp0bpvCnnZ8AbyxCmVPTaPuPf/xj4DwB+sOHHXZY4BwHBTvssEPQrVu34JBDDglcF23w2Wefef3FQQ9YtT6z6TFkyJDg6quvDpw7Vp+39FNvysYbb+x7P5yzosB5wUqKkolbepnuO/EGrJvSV5k2pUeywFUbmZg2dR2oRegc0wQystJLvTcq5+abbw7U2naOlYL//d//Ddya7n7fuZxNGvD0cl23pq8n5angutqDRCLhWWv/9ttv970qyvt73/ueDvmgVrtaXAqp11Su+upFYuedd/Z1pzzuvPPOQNe3G/7QbjLkek9lq9ds17IKch4Tg913393fK26Z3MB5pUy24FN1yxbv448/9nWge0lG86CDDvIs06+d5q7XpOKrNmTEnPMWfw84J0SBGxZJRkk18Nniuc8fA/eNeKDrwPk8CNya6T6PfK45JfjlL3/p7xu9XOtFvakWfC73cVKJVRt33HGHf5bpmebczKaf9vt6rrrltz1X9ZCmG3i14PU8i1vAwEekRvVAleFLf0ili6eHoLrvZUzcykeBjIweyArqGpRBfeKJJwK1nvTAcY5zAjfeHrhxL29U1YJXD8FZZ50VnHjiiYHzGx6kdhvrnFvBLrjwwgsD53gncEvY+havXiJSu1OVVg/V999/P3Brfwfbb799spvriiuuCJwnM2809JCSMdFLh4LepCWjGwNMvhD4E+6PGxP0Rvziiy8O3DhmICPllm8MXnnllUDGJFXmME34q66973//+35XOjtPdoFeTPTy4pbi9YZZLzDSXy8X6lJWyKaH2OpFSlylox72m2yyief1xRdfeKMf9gY0xU1yp5aZyjqbvpKtKT10LjWkM812HehFUV2UGppxq+r5lx6Vo94d9RY5N8neSIuZdNZQioZyVB/pQedk0FKDXubCXiLn9tO/eMmAqRdKLMRd14auC4XUaypXfWXoZOB/9KMfBXq50EvfuHHjUsXw27neU9nqNdu1rHtML0QyetpWb4GGutK76JuLt88++wSnnnqqvy9VPzKkao3me72mAtD9rhcG3Uu6j/USpJfdd955x0cLDXxz8TTkoiEYPW8km14KdY3kc825hbP8i7Ve8t2cDP+saMrA53Ifp+qpbb2E6D7W88HN70g/3Wg/k4F/+eWX/Yt3o8gVfgADH5EKVEtKF17Yis4kloyIHiAyjmHQw0lv/Qpqcar1q1a+/n/yySc+T7c+tz8vY6QuPwU9QMI31lSjIzmcm8YGxldpdEOHD2M9qPUyohZ4WJaMcWjs9FBUqyYMuqn1UA9DU117envXAySVgVqbevgppMoc5hX+pj8Yfvvb34an/MuKWhwKqV2ezekhA3/VVVcl81EXnlrgCnpojhkzJmjXrp3fz8YttcxU1s3pK4PXlB6+0JQ/qUyzXQd6SOsaktENg8pxCxyFu/6lRoYhDHpZCo12eEy/Mhp77rln6iH/kFWvkoywWlRhD5J6TMRBLxXqKQhDeE1pPx99P/jgAz8/RS3Tprpmc7mnVG62es12Ld91112+90p5KOilT/dFuoFvLp5bl8K/gOu611yFI4880r+AKc/Ua6e561Xxw6Ay1R2tF7PwHtV1EbIKDXy2eJJHc4BSx+qdZ81kneZ6zalcGd8wqEcnVwOf6/XfXBd9WLZ+Mxl4PSv14quXhTgFHEa7p10Uglx+ulayaX1tZygbiPTf//7X3EVp+nUXoblu9eR5uQh1ht/PAlVarWesBWvC4Lq6k7N6w2PZfidPnmyuNd7A/73rMWiQJFxQxPUQmOuSTZ6TbO6h4PfdwyV5XH6d3QM/ud/UhmuN2W677dYgT+nnXlCaStLk8dTy5fs/U/m56OF6M5JlOGNurnvRz5R2DwG/2l+oby7ckhmt2shF31z0SM8323Ug/9rOCDVaDCZ19Tf38DVnAJLZul4U/9VF8sCqDfeyYq6V2OCw/NK7ly1zrX/Tted6S/z5/fff31wryX8Vou2mQq76up4Uc4bQXPesOSOYMbtc7inJmK1elXGqTKnX8pQpU8yN3SbLdgavAbfwRHPxtPqe65kzuaUWL11TrickTJ78be56Vb2Gwb2I+pniupdTQ69evVJ3LVs8Z/S8G9dddtklmca9hCW3UzeyXXPSP3URF/fyn5o063Yq+6bu4/QMtBqcvhRS0Foe4VcN6fFS93Ud68si9+Jrrjcr9VRFb397RVS0GpUvvPwf6+Zzb8uNlHGzw/0iHuFF+Pbbbyfj6BMlpXNdoOZav3bSSSeZHgThf32qk+2Bmsxo1YZuCNdVq56d5Klbb73VdLOHQQ9FBX3GF5YjA/fSSy9546FzqYY/NS+daypIv9RV9hRP+hVyw6WWr3wyyZCLHqmLpujB4VpDps9s9LIlYx/mmws3yZEactE3Fz1S89R2c9eB8kzPVy9n2UKoZ2ocrYLoWr+ph8x155quT63QlXrdafvf//63fwFNPd4gsdtJlytTuUojY3jffff5hW5cV316Nn4/l3tKEbPVq86nypQqj17EU69XN+ZvMnTpIVs8fTLo5srYoYce6tNKLxnA1HLC/HK5XsO4ugZ0fclghfeoXrq05kVqyBYvXIXRdc8nk+h5opUa00O2a07XSSonGfxcQyp7pcnEJT0vN7zoy1OZmWRNj699Xce61+O2CiIGPlNtl+mYDKnWg9d3mfpeWt8Hu0ld/ttiNwnEG3ItNfv73//et4T0ZuomMpkbw/MSqxXgJi+ZGxf3+3oAurFK/xaeq0pqgbnueP/wVBoZbd0wrrs1mYVaKlp5yU1AM33TqjdfNzRgbi5AMk62DbWCtLBOetCDToZTPgAU9F27vhcO9UuPX8i+WrCSWb7T89XDjR/7xYDUypLOqgf9qsWVjVtqmakyF1PfVKbFuA5S5Wxq23WvJ6+1MI50deP35oZsGhh4GX69rLkhAm/AwviF/Kqn6/jjj/fX5V//+lf/wuUm3mXMqrl7Somy1WvGTFcdlE5uLkHSr4EbssjotyJbPNft7q9FxXFj5N4gu+EQ7yNAxaReO/lcr26SrP/OXc8HGUXJqda3G95roFK2eOrJ0XXtJqz655EWZXFd4b6nUZnkes25SYPmPnH1Lxp6tkimYgdxCp8perFzw2v+f66rSuqZqV4r9ZTGKdTESZlK10UtGzembm7cybfC3LilqWUsgxcaWDdm5ltIetPUeTfByKeR7loRSd2WesCq1fCLX/zC30x6MOQa1Bugh6WbMGTqtlWe6u7SzZ4adJOqVa/4G264oe8OzHUVMTej2rdapGdqcBPxfJerG3P3KzvJALpPsMzNgk+N1qLtkJl6PPQClY8eevly38z6ZVElq4ZKxEV1lI1bapl60IahmPqmMi3GdRDKmO1XD0+1QFN1UnwN6cixjJuEmUwuYyB9d9ppJ98lnjxRwIZ6TlR/bqzf3xd60VIrXi3V9JDLPZWtXtPzS93Xde8mkJmGqtSVrJeaTF3r2eKp50f6yNC6uQmm3jrnT8BfUyor9drJ53rVS6he9n/2s5/5+1hDCVrS142Hp6rghwSyxXPj5f6lTM8bLQd88MEHe1mVSa7XnJ4lzvdE8rmUz/OogbBZdnStSQ8N6RUS1EuhF9a4BRabiWiN6sGp7ql0wxqKq65indODLj24z368wQ279NLP57Kvt371EKibL1tQ60djmE3J2VRa9VDobTl13DCMq7LlZUsPzfQuujBOS3/VGtFbfxjy0UOyqUuyKdmb4pZeZlh2sfRNZ1qM6yCUsalf92miZ6GepqiH5u6pbPWaTTfVn1qPzd1v2eKpe19j8TL4mUL6tZPv9ar7uLl7Sfo3FU/PG/UwuE9MG4iXzzWn3grNhUm97xpk1sIdMVSPWr6tcNWLWvzuiyHf49lCMSKVHAMfqepAGAhUFgE3c9xPjFT3e6aXzcrSBmmrkYD7GsgPi6oHIG6BLvq41Sj6QKCEBDR0pC5uzZAnQKASCWjyp3PEU4miNyszLfhmEREBAhCAAAQgUHkEaMFXXp0hMQQgAAEIQKBZAhj4ZhERAQIQgAAEIFB5BDDwlVdnSAwBCEAAAhBolgAGvllERIAABCAAAQhUHoH/BzuvDeKnMJbfAAAAAElFTkSuQmCC" alt="plot of chunk unnamed-chunk-1"/></p>

<pre><code class="r">ggsave(&quot;fig_a22.pdf&quot;,
       width = 9, height = 4)
</code></pre>

<pre><code>## `stat_bin()` using `bins =
## 30`. Pick better value with
## `binwidth`.
</code></pre>

<pre><code class="r">## general election, incumbency status of party


## get a data frame on the level of constituencies that indicates 
## the treatment for this constituency

dat_general_partyincumbent_treated &lt;- dat_general_raw %&gt;% 
    filter(winner_loser_before != &quot;Untreated&quot;) %&gt;% 
    mutate(const_election_id = paste(election_id, constituency_name, sep = &quot;_&quot;))


dat_general_partyincumbent_treated$incumbency_status_party &lt;- factor(dat_general_partyincumbent_treated$incumbency_status_party,
                                                                     levels = c(&quot;Party: Opposition&quot;, &quot;Party: Government&quot;))

dat_general_partyincumbent_constituencies_treated &lt;- dat_general_partyincumbent_treated %&gt;% 
    ungroup() %&gt;% 
    select(election_id, constituency_name, winner_loser_before) %&gt;% 
    unique() %&gt;% 
    arrange(election_id, constituency_name) %&gt;% 
    mutate(const_election_id = paste(election_id, constituency_name, sep = &quot;_&quot;))

nrow(dat_general_partyincumbent_constituencies_treated)
</code></pre>

<pre><code>## [1] 169
</code></pre>

<pre><code class="r">## draw 1000 random seeds
set.seed(2356)
seeds &lt;- sample(1:1000, replace = FALSE)

dat_coefficients_permutation_general_partyincumbent &lt;- data.frame()

for (i in seeds) {

    ## cat(&quot;Permutating treatments using seed #&quot;, i, &quot;\n&quot;)
    set.seed(i)

    ## for each election (group_by(election_id)) reshuffle the treatments on the level of
    ## constituencies 
    dat_const_resampled_outcome_general_partyincumbent &lt;- dat_general_partyincumbent_constituencies_treated %&gt;% 
        group_by(election_id) %&gt;% 
        mutate(winner_loser_before_simulated = winner_loser_before[sample(row_number())], 
               replace = FALSE) %&gt;% 
        ungroup() %&gt;% 
        select(const_election_id, winner_loser_before_simulated) ## select constituency identified and simulated treatment

    dat_general_partyincumbent_joined &lt;- left_join(dat_general_partyincumbent_treated, dat_const_resampled_outcome_general_partyincumbent,
                                                   by = &quot;const_election_id&quot;)

    ## run regression model with simulated treatment
    lm_diff_share_sampled_general_partyincumbent &lt;- estimatr::lm_robust(
        diff_share ~ 
            winner_loser_before_simulated * incumbency_status_party +
            party_recoded + election_id + 
            county_gaa,
        clusters = county_gaa,
        se_type = &quot;stata&quot;,
        data = dat_general_partyincumbent_joined)

    ## tidy regression output
    lm_result_tidy_general_partyincumbent &lt;- broom::tidy(lm_diff_share_sampled_general_partyincumbent)

    ## indicator for seed
    lm_result_tidy_general_partyincumbent$seed &lt;- i

    ## bind siimulations
    dat_coefficients_permutation_general_partyincumbent &lt;- bind_rows(lm_result_tidy_general_partyincumbent,
                                                                     dat_coefficients_permutation_general_partyincumbent)
}


## run regression model with real treatment
lm_cand_share_true_general_partyincumbent_all &lt;- estimatr::lm_robust(
    diff_share ~ 
        winner_loser_before * incumbency_status_party +
        party_recoded + election_id + 
        county_gaa,
    clusters = county_gaa,
    se_type = &quot;stata&quot;, 
    data = dat_general_partyincumbent_treated)



## get coefficient of interaction between simulated treatment and incumbent
dat_coefficients_permutation_general_partyincumbent_int &lt;- filter(dat_coefficients_permutation_general_partyincumbent,
                                                                  term == &quot;winner_loser_before_simulatedWin:incumbency_status_partyParty: Government&quot;) 


## number of simulations (nrow should be 1,000)
nrow(dat_coefficients_permutation_general_partyincumbent_int)
</code></pre>

<pre><code>## [1] 1000
</code></pre>

<pre><code class="r">## get coefficient of &quot;true&quot; interaction 
lm_result_tidy_true_general_partyincumbent &lt;- broom::tidy(lm_cand_share_true_general_partyincumbent_all)

lm_result_tidy_true_general_partyincumbent &lt;- filter(lm_result_tidy_true_general_partyincumbent, 
                                                     term == &quot;winner_loser_beforeWin:incumbency_status_partyParty: Government&quot;)

## Figure A23 ----

#                   # create a plot with the distribution of simulated coefficients
## and the true interaction coefficient
ggplot(dat_coefficients_permutation_general_partyincumbent_int, aes(x = estimate)) +
    geom_histogram(colour = &quot;grey50&quot;, fill = &quot;grey50&quot;) + 
    geom_vline(data = lm_result_tidy_true_general_partyincumbent, 
               aes(xintercept = estimate), 
               size = 1.5, 
               colour = &quot;red&quot;) +
    geom_vline(xintercept = 0, linetype = &quot;dashed&quot;,
               colour = &quot;black&quot;) +
    scale_x_continuous(limits = c(-2.3, 2.3)) +
    annotate(&quot;text&quot;, x = -0.69,
             colour = &quot;white&quot;,
             y = 7, label = &quot;Coefficients using\nrandomly permutated treatments&quot;) +
    annotate(&quot;text&quot;, x = lm_result_tidy_true_general_partyincumbent$estimate + 0.04,
             hjust = 0,
             colour = &quot;red&quot;,
             y = 100, label = &quot;Coefficient using real treatment&quot;) +
    labs(x = &quot;Coefficient of interaction term (Win x Party: Incumbent Government)&quot;, y = &quot;Count&quot;) 
</code></pre>

<pre><code>## `stat_bin()` using `bins =
## 30`. Pick better value with
## `binwidth`.
</code></pre>

<p><img 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" alt="plot of chunk unnamed-chunk-1"/></p>

<pre><code class="r">ggsave(&quot;fig_a23.pdf&quot;,
       width = 9, height = 4)
</code></pre>

<pre><code>## `stat_bin()` using `bins =
## 30`. Pick better value with
## `binwidth`.
</code></pre>

<pre><code class="r">## script executed successfully on
date()
</code></pre>

<pre><code>## [1] &quot;Wed Jun 23 09:58:58 2021&quot;
</code></pre>

<pre><code class="r">sessionInfo()
</code></pre>

<pre><code>## R version 4.0.2 (2020-06-22)
## Platform: x86_64-apple-darwin17.0 (64-bit)
## Running under: macOS  10.16
## 
## Matrix products: default
## LAPACK: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRlapack.dylib
## 
## locale:
## [1] en_IE.UTF-8/en_IE.UTF-8/en_IE.UTF-8/C/en_IE.UTF-8/en_IE.UTF-8
## 
## attached base packages:
## [1] grid      stats    
## [3] graphics  grDevices
## [5] utils     datasets 
## [7] methods   base     
## 
## other attached packages:
##  [1] forcats_0.5.1     
##  [2] haven_2.3.1       
##  [3] scales_1.1.1      
##  [4] Hmisc_4.4-2       
##  [5] Formula_1.2-4     
##  [6] lattice_0.20-41   
##  [7] parameters_0.13.0 
##  [8] MuMIn_1.43.17     
##  [9] ebal_0.1-6        
## [10] jtools_2.1.2      
## [11] lubridate_1.7.10  
## [12] broom.mixed_0.2.6 
## [13] broom_0.7.4       
## [14] lme4_1.1-26       
## [15] survey_4.0        
## [16] survival_3.2-7    
## [17] Matrix_1.3-2      
## [18] WeightIt_0.11.0   
## [19] cobalt_4.2.4      
## [20] car_3.0-10        
## [21] carData_3.0-4     
## [22] cowplot_1.1.1     
## [23] here_1.0.1        
## [24] modelsummary_0.8.0
## [25] ggeffects_1.0.1   
## [26] estimatr_0.30.2   
## [27] ggplot2_3.3.3     
## [28] stringr_1.4.0     
## [29] tidyr_1.1.3       
## [30] dplyr_1.0.5       
## [31] knitr_1.31        
## 
## loaded via a namespace (and not attached):
##   [1] readxl_1.3.1       
##   [2] uuid_0.1-4         
##   [3] backports_1.2.1    
##   [4] systemfonts_1.0.1  
##   [5] plyr_1.8.6         
##   [6] TMB_1.7.19         
##   [7] splines_4.0.2      
##   [8] TH.data_1.0-10     
##   [9] digest_0.6.27      
##  [10] htmltools_0.5.1.1  
##  [11] fansi_0.4.2        
##  [12] magrittr_2.0.1     
##  [13] checkmate_2.0.0    
##  [14] cluster_2.1.0      
##  [15] openxlsx_4.2.3     
##  [16] officer_0.3.16     
##  [17] sandwich_3.0-0     
##  [18] jpeg_0.1-8.1       
##  [19] colorspace_2.0-0   
##  [20] rvest_0.3.6        
##  [21] ggrepel_0.9.1      
##  [22] mitools_2.4        
##  [23] xfun_0.20          
##  [24] crayon_1.4.1       
##  [25] zoo_1.8-8          
##  [26] glue_1.4.2         
##  [27] kableExtra_1.3.1   
##  [28] gtable_0.3.0       
##  [29] emmeans_1.5.4      
##  [30] webshot_0.5.2      
##  [31] abind_1.4-5        
##  [32] annotater_0.1.3    
##  [33] mvtnorm_1.1-1      
##  [34] DBI_1.1.1          
##  [35] Rcpp_1.0.6         
##  [36] viridisLite_0.4.0  
##  [37] xtable_1.8-4       
##  [38] htmlTable_2.1.0    
##  [39] foreign_0.8-81     
##  [40] stats4_4.0.2       
##  [41] htmlwidgets_1.5.3  
##  [42] httr_1.4.2         
##  [43] RColorBrewer_1.1-2 
##  [44] ellipsis_0.3.2     
##  [45] pkgconfig_2.0.3    
##  [46] farver_2.1.0       
##  [47] nnet_7.3-15        
##  [48] utf8_1.2.1         
##  [49] tidyselect_1.1.1   
##  [50] labeling_0.4.2     
##  [51] rlang_0.4.11       
##  [52] reshape2_1.4.4     
##  [53] munsell_0.5.0      
##  [54] cellranger_1.1.0   
##  [55] tools_4.0.2        
##  [56] cli_2.5.0          
##  [57] generics_0.1.0     
##  [58] sjlabelled_1.1.7   
##  [59] evaluate_0.14      
##  [60] tables_0.9.6       
##  [61] zip_2.1.1          
##  [62] pander_0.6.3       
##  [63] purrr_0.3.4        
##  [64] nlme_3.1-152       
##  [65] mime_0.10          
##  [66] xml2_1.3.2         
##  [67] compiler_4.0.2     
##  [68] rstudioapi_0.13    
##  [69] curl_4.3           
##  [70] png_0.1-7          
##  [71] tibble_3.1.1       
##  [72] statmod_1.4.35     
##  [73] stringi_1.5.3      
##  [74] highr_0.8          
##  [75] gdtools_0.2.3      
##  [76] texreg_1.37.5      
##  [77] nloptr_1.2.2.2     
##  [78] markdown_1.1       
##  [79] vctrs_0.3.8        
##  [80] pillar_1.6.0       
##  [81] lifecycle_1.0.0    
##  [82] estimability_1.3   
##  [83] data.table_1.14.0  
##  [84] insight_0.14.0     
##  [85] flextable_0.6.3    
##  [86] R6_2.5.0           
##  [87] latticeExtra_0.6-29
##  [88] gridExtra_2.3      
##  [89] rio_0.5.16         
##  [90] codetools_0.2-18   
##  [91] boot_1.3-27        
##  [92] MASS_7.3-53        
##  [93] assertthat_0.2.1   
##  [94] rprojroot_2.0.2    
##  [95] withr_2.4.2        
##  [96] multcomp_1.4-16    
##  [97] mgcv_1.8-33        
##  [98] bayestestR_0.8.2   
##  [99] hms_1.0.0          
## [100] rpart_4.1-15       
## [101] coda_0.19-4        
## [102] minqa_1.2.4        
## [103] rmarkdown_2.6      
## [104] snakecase_0.11.0   
## [105] base64enc_0.1-3
</code></pre>

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